Foundations of Geometric Deep Learning

Abstract

Geometric deep learning aims to generalize neural models to non-Euclidean domains such as graphs and manifolds. The field has made promising advances and remarkable performance improvements, especially in studying social networks, recommendation systems, drug discovery, anomaly detection, and urban intelligence. In this project, we develop a foundational understanding of geometric deep learning, its capabilities, limitations, and applications. We build on and advance the theory of graph limits to study the robustness, transferability, and scalability of Graph Neural Network (GNN) learning models. In particular, we will take advantage of the powerful analytical and algorithmic toolkit developed for graphons to analyze the performance of graph neural networks on graph-structured data.

Document Details

Document Type
DoD Grant Award
Publication Date
Feb 29, 2024
Source ID
FA95502310251

Entities

People

  • Amin Saberi

Organizations

  • Air Force Office of Scientific Research
  • Stanford University
  • United States Air Force

Tags

Fields of Study

  • Computer science

Readers

  • Distributed Systems and Data Platform Development
  • Graph Algorithms and Convex Optimization.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks